SoroushVahidi/scidocs huggingface dataset:
k-Shape: Efficient and Accurate Clustering of Time Series
(2016)

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we describe k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. An extensive experimental evaluation against partitional, hierarchical, and spectral clustering methods, with the most competitive distance measures, showed the robustness of k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and efficient clustering approach for time series with broad applications.
Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter
(2012)

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors.
Autonomous collision avoidance for unmanned surface ships using onboard monocular vision
(2015)

This study presents the development of vision-based techniques for autonomous collision avoidance by an unmanned surface ship using an onboard monocular camera. In order to determine the initiation of an evasive maneuver, the range and bearing measurements of each target traffic ship with respect to the observer (e.g., own ship) need to be provided for trajectory estimation. A tracking estimator is used to estimate the target ship trajectory in the framework of bearings-only tracking based on the extended Kalman filter (EKF) algorithm with a Continuous White Noise Acceleration (CWNA) model. To enhance the observability of the tracking filter, the vertical pixel distance from the water horizon to each target ship is used as a range measurement. When the estimated separation distance between the target and own ships is less than a predefined minimum separation, the own ship alters its heading angle to avoid an imminent collision following the standard marine traffic rules. Field experiment results are presented and discussed to demonstrate the feasibility and validity of the proposed approach.
Enhancing the Effectiveness of Work Groups and Teams.
(2006)

Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern. Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research-literally thousands of studies-focused on understanding and influencing the processes that underlie team effectiveness. Our goal in this monograph is to sift through this voluminous literature to identify what we know, what we think we know, and what we need to know to improve the effectiveness of work groups and teams. We begin by defining team effectiveness and establishing the conceptual underpinnings of our approach to understanding it. We then turn to our review, which concentrates primarily on topics that have well-developed theoretical and empirical foundations, to ensure that our conclusions and recommendations are on firm footing. Our review begins by focusing on cognitive, motivational/affective, and behavioral team processes-processes that enable team members to combine their resources to resolve task demands and, in so doing, be effective. We then turn our attention to identifying interventions, or "levers," that can shape or align team processes and thereby provide tools and applications that can improve team effectiveness. Topic-specific conclusions and recommendations are given throughout the review. There is a solid foundation for concluding that there is an emerging science of team effectiveness and that findings from this research foundation provide several means to improve team effectiveness. In the concluding section, we summarize our primary findings to highlight specific research, application, and policy recommendations for enhancing the effectiveness of work groups and teams.
Data-Driven Modeling for Chinese Ancient Architecture
(2017)

The existing 3D modeling studies of Chinese ancient architecture are mostly procedure driven and rely on fixed construction rules. Therefore, these methods have limited applications in virtual reality (VR) engineering. We propose a data-driven approach to synthesize 3D models from existing 3D data that provides more flexibility and fills the gap between academic studies and VR engineering. First, 3D architecture models were preprocessed and decomposed into components, and the components were clustered by their geometric features. Second, a Bayesian network was generated by learning from the dataset to represent the internal relationships between the architectural components. Third, the inference results of the trained network were utilized to generate a reasonable relationship matching to support the synthesis of the structural components. The proposed method can be used in 3D content creation for VR development and directly supports VR applications in practice.
The Anisotropic Noise in Stochastic Gradient Descent : Its Behavior of Escaping from Minima and Regularization Effects
(2018)

Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.
Ka-Band Linear to Circular Polarization Converter Based on Multilayer Slab With Broadband Performance
(2017)

In this paper, a Ka-band polarization converter is presented, which is based on multilayer slab. In order to improve impedance matching, metallic circular traces are printed periodically on each dielectric multilayer slab. Simulated results of the polarizer show that it can transform linearly polarized (LP) to circularly polarized (CP) fields over a frequency band from 23 to 35GHz (42%) with an insertion loss less than 0.5 dB. The transmitted CP wave by the polarizer is approximately robust under oblique illuminations. The polarizer is fabricated and measured by a wideband horn antenna satisfying the simulated results. Next, in order to design a high-gain CP structure around 30 GHz, an 8-element LP array antenna with Chebyshev tapered distribution is designed and integrated with the polarizer. Obviously, the antenna limits the overall bandwidth (nearly 28 to 31.5 GHz) due to the narrowband nature of the LP antenna array. When the polarizer is illuminated by an incident LP wave, the two linear components of the transmitted wave with approximately equal amplitudes and 90° phase difference on the frequency band of interest are produced. Experimental results of the proposed structure show a pure CP with a gain of 13 dBi at 30 GHz, which can be suitable for millimeter wave communication.
Sequence-to-Dependency Neural Machine Translation
(2017)

Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and JapaneseEnglish translation tasks.
Overdose Fatalities Patterns of Abuse Among Unintentional Pharmaceutical
(2008)

Supplementary material http://jama.ama-assn.org/cgi/content/full/300/22/2613/DC1 Report Video JAMA Correction Contact me if this article is corrected. Citations Contact me when this article is cited. This article has been cited 1 time. Topic collections Contact me when new articles are published in these topic areas. Drug Therapy, Other Pain; Public Health; Substance Abuse/ Alcoholism; Drug Therapy; Adverse Effects;
A Fiber Tracking Method for Building Patient Specific Dynamic Musculoskeletal Models from Diffusion Tensor Data
(2008)

A new musculoskeltal simulation primitive, the strand, can provide an efficient way of simulating complicated muscle fiber architectures [1]. In this paper we present a new fiber tracking algorithm based on an energy minimizing active curve that is well suited for building these strand type models from diffusion tensor imaging data. We present fiber tracking results for the Brachioradialis muscle in the left forearm of a male subject. The algorithm produces a space filling arrangement of fibers that are uniformly distributed throughout the muscle and aligned with the underlying muscle fiber direction.
Brief screening instrument for post-traumatic stress disorder.
(2002)

BACKGROUND
Brief screening instruments appear to be a viable way of detecting post-traumatic stress disorder (PTSD) but none has yet been adequately validated.


AIMS
To test and cross-validate a brief instrument that is simple to administer and score.


METHOD
Forty-one survivors of a rail crash were administered a questionnaire, followed by a structured clinical interview 1 week later.


RESULTS
Excellent prediction of a PTSD diagnosis was provided by respondents endorsing at least six re-experiencing or arousal symptoms, in any combination. The findings were replicated on data from a previous study of 157 crime victims.


CONCLUSIONS
Performance of the new measure was equivalent to agreement achieved between two full clinical interviews.
Treatment with the Self-Discovery Camp (SDiC) improves Internet gaming disorder.
(2017)

INTRODUCTION
Internet gaming disorder (IGD) is a novel behavioral addiction that influences the physical, mental, and social aspects of health due to excessive Internet gaming. One type of intensive treatment for IGD is the therapeutic residential camp (TRC), which comprises many types of therapies, including psychotherapy, psychoeducational therapy, and cognitive behavioral therapy. The TRC was developed in South Korea and has been administered to many patients with IGD; however, its efficacy in other countries remains unknown. We investigated the efficacy of the Self-Discovery Camp (SDiC), a Japanese version of a TRC, and the correlations between individual characteristics and outcome measures.


METHODS
We recruited 10 patients with IGD (all male, mean age=16.2years, diagnosed using the DSM-5) to spend 8 nights and 9days at the SDiC. We measured gaming time as well as self-efficacy (using the Stages of Change Readiness and Treatment Eagerness Scale, a measure of therapeutic motivation and problem recognition).


RESULTS
Total gaming time was significantly lower 3months after the SDiC. Problem recognition and self-efficacy towards positive change also improved. Furthermore, there was a correlation between age of onset and problem recognition score.


CONCLUSIONS
Our results demonstrate the effectiveness of the SDiC for IGD, especially regarding gaming time and self-efficacy. Additionally, age of onset may be a useful predictor of IGD prognosis. Further studies with larger sample sizes and control groups, and that target long-term outcomes, are needed to extend our understanding of SDiC efficacy.
Incorporating Tweet Relationships into Topic Derivation
(2015)

With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweettopic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information. Keywords-Topic Derivation; Twitter; Tweets Relationship;
SHORTEST PATHS FOR THE REEDS-SHEPP CAR : A WORKED OUT EXAMPLE OF THE USE OF GEOMETRIC TECHNIQUES IN NONLINEAR OPTIMAL CONTROL
(1991)

We illustrate the use of the techniques of modern geometric optimal control theory by studying the shortest paths for a model of a car that can move forwards and backwards. This problem was discussed in recent work by Reeds and Shepp who showed, by special methods, (a) that shortest path motion could always be achieved by means of trajectories of a special kind, namely, concatenations of at most five pieces, each of which is either a straight line or a circle, and (b) that these concatenations can be classified into 48 three-parameter families. We show how these results fit in a much more general framework, and can be discovered and proved by applying in a systematic way the techniques of Optimal Control Theory. It turns out that the “classical” optimal control tools developed in the 1960’s, such as the Pontryagin Maximum Principle and theorems on the existence of optimal trajectories, are helpful to go part of the way and get some information on the shortest paths, but do not suffice to get the full result. On the other hand, when these classical techniques are combined with the use of a more recently developed body of theory, namely, geometric methods based on the Lie algebraic analysis of trajectories, then one can recover the full power of the Reeds-Shepp results, and in fact slightly improve upon them by lowering their 48 to a 46.
Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy
(2015)

In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.
Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features
(2017)

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.
A Simple and Reliable PDMS and SU-8 Irreversible Bonding Method and Its Application on a Microfluidic-MEA Device for Neuroscience Research
(2015)

Polydimethylsiloxane (PDMS) and SU-8 are currently two very commonly used polymeric materials in the microfluidics field for biological applications. However, there is a pressing need to find a simple, reliable, irreversible bonding method between these two materials for their combined use in innovative integrated microsystems. In this paper, we attempt to investigate the aminosilane-mediated irreversible bonding method for PDMS and SU-8 with X-Ray Photoelectron Spectroscopy (XPS) surface analysis and bonding strength tests. Additionally, the selected bonding method was applied in fabricating a microelectrode array (MEA) device, including microfluidic features, which allows electrophysiological observations on compartmentalized neuronal cultures. As there is a growing trend towards microfluidic devices for neuroscience research, this type of integrated microdevice, which can observe functional alterations on compartmentalized neuronal culture, can potentially be used for neurodegenerative disease research and pharmaceutical development.
Measurements and Simulations of the Convective Heat Transfer Coefficients on the End Windings of an Electrical Machine
(2012)

The prediction of the thermal behavior of an electrical machine in the design process basically depends on the used boundary conditions such as the convective heat transfer coefficient. Due to the complicated shape of the end windings, the heat transfer coefficient has to be identified empirically by using thermopiles as heat flux sensors placed at certain positions on the end windings, because an analytical derivation is not feasible. This paper presents the results of the measured values of the heat transfer on the end windings of an electrical machine. The gained coefficients are compared to the results obtained by numerical simulations using commercial software for computational fluid dynamics. The simulation results are discussed according to the validity of the gained heat transfer coefficient. The configuration of the simulation has been tested on a cylindrical shape in a cross-flow.
Modeling Credit Risk for SMEs : Evidence from the US Market
(2006)

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model’s ability to lower bank capital requirements considering the new Basel Capital Accord’s rules for SMEs.
Query optimization using column statistics in hive
(2011)

Hive is a data warehousing solution on top of the Hadoop MapReduce framework that has been designed to handle large amounts of data and store them in tables like a relational database management system or a conventional data warehouse while using the parallelization and batch processing functionalities of the Hadoop MapReduce framework to speed up the execution of queries. Data inserted into Hive is stored in the Hadoop FileSystem (HDFS), which is part of the Hadoop MapReduce framework. To make the data accessible to the user, Hive uses a query language similar to SQL, which is called HiveQL. When a query is issued in HiveQL, it is translated by a parser into a query execution plan that is optimized and then turned into a series of map and reduce iterations. These iterations are then executed on the data stored in the HDFS, writing the output to a file.
 The goal of this work is to to develop an approach for improving the performance of the HiveQL queries executed in the Hive framework. For that purpose, we introduce an extension to the Hive MetaStore which stores metadata that has been extracted on the column level of the user database. These column level statistics are then used for example in combination with join ordering algorithms which are adapted to the specific needs of the Hadoop MapReduce environment to improve the overall performance of the HiveQL query execution.